United States

argument

automatic scoring

assessment

machine learning

The 2020 AAPT Virtual Summer Meeting brought together physics educators from around the globe to learn about the latest in physics theory, research methodology and education from leaders in their field. The program featured plenary speakers, sessions, workshops and many special activities.

For more information please visit the 2020 AAPT Virtual Summer Meeting page at: https://www.aapt.org/Conferences/SM2020/index.cfm.


AAPT SM20

**Making Physics More Inclusive and Eliminating Exclusionary Practices in Physics**

As a gateway discipline to STEM majors and careers, physics plays a central role in deciding who does, and who doesn't, get to discover and pursue scientific careers. Technical and scientific professionals are rewarded with economic privilege and status which affords them both social and cultural power. Therefore, social justice will be realized only when the opportunity, and the choice, to excel in physics is open to all students. The culture and practices of the physics classroom reflect the culture of physics at large, which raises the question of how our professional physics communities can embrace, rather than inadvertently repel, a diverse group of academics that can help improve physics culture.  To this end, the 2021 Physics Education Research Conference will focus on defining inclusive spaces and practices. Additionally, we will explore strategies for eliminating exclusionary practices in our classrooms, research spaces, and professional organizations. We call on the community to propose sessions that will drive conversations related to these initiatives throughout PERC 2021.

Please download the full program [here. ](https://drive.google.com/file/d/1TRMC_SYJ3PUqXyP7sskA7LRSA8ow0eRE/view?usp=sharing)

PERC SM21

August 4, 2021 - August 5, 2021 in a Virtual Conference Format

Making Physics More Inclusive and Eliminating Exclusionary Practices in Physics

Recent science education reform calls for argumentation as one of the central practices in science classrooms. However, evaluating student argumentation proficiency is challenging. Based on an existing learning progression of argumentation and grade-appropriate objectives, we developed a measure for middle-school students in three contexts (e.g. kinetic theory of gases) of 19 constructed response items. Experts coded responses from 932 middle-school students for components of argumentation and used these coded responses to train machine learning algorithms for each item. The algorithms were validated using a cross-validation approach. We found that the machine-human agreements are robust, with a mean Cohen’s kappa=0.75, SD=0.08. We further applied a many-facet Rasch analysis and found the item difficulty in this measure is well aligned with the established learning progression. The algorithms developed in this study can be used in classrooms to automatically evaluate students’ argumentation proficiency.

Applying Machine Learning to Automatically Assess Middle-School Students’ Argumentation

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https://d3smihljt9218e.cloudfront.net/transcript/443/subtitle/eb6f98f2f55ca2cd275debd259578ad4.vtt

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Applying Machine Learning to Automatically Assess Middle-School Students’ Argumentation

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